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Lecturer(s)
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Trnečka Martin, doc. RNDr. Ph.D.
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Outrata Jan, doc. Mgr. Ph.D.
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Course content
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1. Intro: Data mining: extracting information from data, KDD, typical tasks. Machine learning: learning from information from data, phases and types. 2. Data: Types of data and attributes, quality a preprocessing (sampling, normalization, discretization), similarity and dissimimlarity of objects, summary statistics a visualization. 3. Classification: Decision trees, overfitting problem, evaluation of performance, rule-based, nearest neighbor, naive Bayes, support vector machines (SVM), regression. 4. Association analysis: Itemsets, rules, Apriori algorithm, interestingness evaluation. 5. Cluster analysis: types of clusters, K-means, hierarchical, density-based, expectation-maximization (EM), quality evaluation.
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Learning activities and teaching methods
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Lecture, Dialogic Lecture (Discussion, Dialog, Brainstorming)
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Learning outcomes
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The course is the first part of the two semester course devoted to principles and main methods of data mining and machine learning. After problem introduction with defining these notions and looking at data and their preprocessing the basic data minig methods of classification, association analysis and clustering used (not only) in machine learning are discussed, from the algorithmic point of view.
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Prerequisites
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unspecified
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Assessment methods and criteria
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unspecified
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Recommended literature
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Han J., Pei J., Tong H. (2022). Data Mining: Concepts and Techniques.
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Marsland S. (2014). Machine Learning: An Algorithmic Perspective (2nd edition).
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Murphy K. P. (2022). Probabilistic Machine Learning: An Introduction.
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Poole D. L., Mackworth A. K. (2023). Artificial Intelligence: Foundations of Computational Agents (3rd edition).
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Rokach L., Maimon O. (2015). Data mining with decision trees: theory and applications.
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Tan P.-N., Steinbach M., Kumar V. (2018). Introduction to Data Mining (2nd edition).
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Witten I. H., Frank E., Hall M. A., Pal Ch. J., Foulds J. (2025). Data Mining: Practical Machine Learning Tools and Techniques.
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Zaki M. J., Meira W. Jr. (2020). Data Mining and Analysis: Fundamental Concepts and Algorithms (2nd Edition).
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